After normalisation, the text is divided into small fragments in step 5, and then those fragments are given to the model in step 6. If the entire document is supplied at once, the model is unable to understand and extract key points from it. Thus the inputs are provided in small fragments to these models. Those outputs later merged for the actual summarised document.We have used two models. The first one is BART. It is used for extractive summarization. BART is described as a denoising autoencoder which is implemented as a sequence-to-sequence model and a bi-directional encoder [1]}.
BART can be used in many downstream applications. K. M. Hermann et al. in [2]} proposed a new methodology to fine-tune BART for Comprehension, Translation and Natural Language Generation with very less amount of training data. The model is built for summarization in English texts based on that paper.
The second model is PEGASUS. It is a pre-trained model with extracted Gap-sentences for abstractive summarization. Good abstractive summarization performance can be achieved across a broad domain with very little supervision by fine-tuning PEGASUS [3]}.
Next, in step 7 the model outputs for all the small fragments are merged and in step 8 finally, we get the summarised document.
The model outputs at the next stage are evaluated by different Legal experts on multiple parameters.
